Instructions to use olm/olm-roberta-base-latest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use olm/olm-roberta-base-latest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="olm/olm-roberta-base-latest")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("olm/olm-roberta-base-latest") model = AutoModelForMaskedLM.from_pretrained("olm/olm-roberta-base-latest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from olm/olm-roberta-base-latest: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/olm/olm-roberta-base-latest/resolve/main/training_args.bin
- Command line
-
hf download hf://olm/olm-roberta-base-latest/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/olm/olm-roberta-base-latest/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- 796d03bc89dccdafa3a7b591a38b50ca2e66837452a9ff2ead8c60ee32f13d12
- Size of remote file:
- 3.45 kB
- SHA256:
- 1820be18be126368b97f91ea51f07b8b4bd2d32b3143c8e878d855ed59c34ae5
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